Local Fast R-CNN Flow for Object-centric Event Recognition in Complex Traffic Scenes

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aut.relation.conferencePSIVT 2017 Workshop on Computer Vision and Modern Vehiclesen_NZ
aut.relation.endpage12
aut.relation.pages12
aut.relation.startpage1
aut.researcherYan, Wei-Qi
dc.contributor.authorGu, Qen_NZ
dc.contributor.authorYang, Jen_NZ
dc.contributor.authorYan, W-Qen_NZ
dc.contributor.authorLi, Yen_NZ
dc.contributor.authorKlette, Ren_NZ
dc.date.accessioned2019-08-21T22:36:29Z
dc.date.available2019-08-21T22:36:29Z
dc.date.copyright2017-11-21en_NZ
dc.date.issued2017-11-21en_NZ
dc.description.abstractThis paper presents a solution for an integrated object-centric event recognition problem for intelligent traffic supervision. We propose a novel event-recognition framework using deep local flow in a fast regionbased convolutional neural network (R-CNN). First, we use a fine-tuned fast R-CNN to accurately extract multi-scale targets in the open environment. Each detected object corresponds to an event candidate. Second, a deep belief propagation method is proposed for the calculation of local fast R-CNN flow (LFRCF) between local convolutional feature matrices of two non-adjacent frames in a sequence. Third, by using the LFRCF features, we can easily identify the moving pattern of each extracted object and formulate a conclusive description of each event candidate. The contribution of this paper is to propose an optimized framework for accurate event recognition. We verify the accuracy of multi-scale object detection and behavior recognition in extensive experiments on real complex road-intersection surveillance videos.
dc.identifier.citationIn: Satoh S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science, vol 10799. Springer, Cham
dc.identifier.doi10.1007/978-3-319-92753-4_34
dc.identifier.urihttps://hdl.handle.net/10292/12758
dc.publisherSpringeren_NZ
dc.relation.urihttps://link.springer.com/chapter/10.1007/978-3-319-92753-4_34#copyrightInformation
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectDeep learning; Event recognition; Convolutional neural network; Belief propagation
dc.titleLocal Fast R-CNN Flow for Object-centric Event Recognition in Complex Traffic Scenesen_NZ
dc.typeConference Contribution
pubs.elements-id314902
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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